




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_big_bird import BigBirdTokenizer
else:
							UpperCAmelCase_		:					Dict          =  None

UpperCAmelCase_		:					Optional[Any]          =  logging.get_logger(__name__)
UpperCAmelCase_		:					Optional[int]          =  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_		:					Dict          =  {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase_		:					Optional[int]          =  {
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}


UpperCAmelCase_		:					Any          =  "▁"




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       VOCAB_FILES_NAMES
						UpperCamelCase      =       PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      =       BigBirdTokenizer
						UpperCamelCase      =       ["""input_ids""", """attention_mask"""]
						UpperCamelCase      =       []

						def __init__(		self :List[str]     ,			__snake_case :List[Any]=None     ,			__snake_case :Tuple=None     ,			__snake_case :Dict="<unk>"     ,			__snake_case :Union[str, Any]="<s>"     ,			__snake_case :List[Any]="</s>"     ,			__snake_case :List[Any]="<pad>"     ,			__snake_case :Optional[Any]="[SEP]"     ,			__snake_case :Union[str, Any]="[MASK]"     ,			__snake_case :Dict="[CLS]"     ,			**__snake_case :Tuple     ,			):
										'''simple docstring'''

										__magic_name__						:						Dict										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else bos_token
										__magic_name__						:						Dict										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else eos_token
										__magic_name__						:						Optional[Any]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else unk_token
										__magic_name__						:						Optional[Any]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else pad_token
										__magic_name__						:						Union[str, Any]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else cls_token
										__magic_name__						:						str										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else sep_token

										# Mask token behave like a normal word, i.e. include the space before it
										__magic_name__						:						Union[str, Any]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else mask_token

										super().__init__(
										    __snake_case     ,			tokenizer_file=__snake_case     ,			bos_token=__snake_case     ,			eos_token=__snake_case     ,			unk_token=__snake_case     ,			sep_token=__snake_case     ,			pad_token=__snake_case     ,			cls_token=__snake_case     ,			mask_token=__snake_case     ,			**__snake_case     ,			)

										__magic_name__						:						List[str]										=vocab_file
										__magic_name__						:						List[str]										=False if not self.vocab_file else True

						def      A__    (		self :Dict     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None			):
										'''simple docstring'''

										__magic_name__						:						Dict										=[self.sep_token_id]
										__magic_name__						:						str										=[self.cls_token_id]
										if token_ids_a is None:
														return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep

						def      A__    (		self :int     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None     ,			__snake_case :bool = False			):
										'''simple docstring'''

										if already_has_special_tokens:
														if token_ids_a is not None:
																		raise ValueError(
																		    """You should not supply a second sequence if the provided sequence of """
																		    """ids is already formatted with special tokens for the model."""			)
														return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

										if token_ids_a is None:
														return [1] + ([0] * len(__snake_case			)) + [1]
										return [1] + ([0] * len(__snake_case			)) + [1] + ([0] * len(__snake_case			)) + [1]

						def      A__    (		self :Tuple     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=[self.sep_token_id]
										__magic_name__						:						Optional[int]										=[self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep			) * [0]
										return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]


						def      A__    (		self :Dict     ,			__snake_case :str     ,			__snake_case :Optional[str] = None			):
										'''simple docstring'''

										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
														    """tokenizer."""			)

										if not os.path.isdir(__snake_case			):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
														return
										__magic_name__						:						Dict										=os.path.join(
										    __snake_case     ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

										if os.path.abspath(self.vocab_file			) != os.path.abspath(__snake_case			):
														copyfile(self.vocab_file     ,			__snake_case			)

										return (out_vocab_file,)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase    :   Tuple						=			{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   int						=			[
		    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'LukeForEntityClassification',
		    'LukeForEntityPairClassification',
		    'LukeForEntitySpanClassification',
		    'LukeForMultipleChoice',
		    'LukeForQuestionAnswering',
		    'LukeForSequenceClassification',
		    'LukeForTokenClassification',
		    'LukeForMaskedLM',
		    'LukeModel',
		    'LukePreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		lowercase    :   Any						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)